7 Sensitivity analysis - Scenario Comparison

EPOCH

RCT Effects

# lsa_int_epoch_df %>% 
#   filter(year==2057) %>% 
#   filter(focus=="Effect BF OR") %>% 
#   filter(age.groups.output=='Age 2') %>%
#   ggplot()+
#   geom_point(aes(x=input.value, y=int.value, color=gender.output), size=1)+
#   geom_line(aes(x=input.value, y=int.value, color=gender.output))+
#   geom_hline(aes(yintercept=det.int.value), linetype="dashed")+
#   facet_grid( rows = vars(age.groups.output,bmi.output), scale="free")+
#   theme_bw()+theme(legend.position = "bottom",
#              panel.spacing.y=unit(4, "mm"))+
#   guides(colour = guide_legend("BMI"))+
#   ggtitle("Effect BF OR")+ylab("Prevalence")+
#   xlab("Input assumption")+
#   scale_y_continuous(labels = scales::percent)+
#   
#   lsa_int_epoch_df %>% 
#   filter(year==2057) %>% 
#   filter(focus=="Effect TV OR") %>% 
#   filter(age.groups.output=='Age 2') %>%
#   ggplot()+
#   geom_point(aes(x=input.value, y=int.value, color=gender.output), size=1)+
#   geom_line(aes(x=input.value, y=int.value, color=gender.output))+
#   geom_hline(aes(yintercept=det.int.value), linetype="dashed")+
#   facet_grid( rows = vars(age.groups.output,bmi.output), scale="free")+
#   theme_bw()+theme(legend.position = "bottom",
#              panel.spacing.y=unit(4, "mm"))+
#   guides(colour = guide_legend("BMI"))+
#   ggtitle("Effect TV OR")+ylab("Prevalence")+
#   xlab("Input assumption")+
#   scale_y_continuous(labels = scales::percent)+
#   
#   lsa_int_epoch_df %>% 
#   filter(year==2057) %>% 
#   filter(focus=="\"Effect Non-core OR\"") %>% 
#   filter(age.groups.output=='Age 2') %>%
#   ggplot()+
#   geom_point(aes(x=input.value, y=int.value, color=gender.output), size=1)+
#   geom_line(aes(x=input.value, y=int.value, color=gender.output))+
#   geom_hline(aes(yintercept=det.int.value), linetype="dashed")+
#   facet_grid( rows = vars(age.groups.output,bmi.output), scale="free")+
#   theme_bw()+theme(legend.position = "bottom",
#              panel.spacing.y=unit(4, "mm"))+
#   guides(colour = guide_legend("BMI"))+
#   ggtitle("Effect Non-core OR")+ylab("Prevalence")+
#   xlab("Input assumption")+
#   scale_y_continuous(labels = scales::percent)+
#   
#   plot_layout(ncol=3, nrow=1, guides = "collect")&theme(legend.position = "bottom")

Scale up

Percentage of infants exposed

Relapse Rate

Relapse Rate EPOCH

CCI

CCI Scale up factor

Childcare settings intervention Change in Diet coefficients

Childcare settings intervention Change in PA coefficients

Percentage of childcare centers participating in intervention

Definition in food groups

SI

Relapse Rate

SI Percentage of Schools participating in intervention

SI percentage of total diet consumed at school

SI Scale up factor

SI % change in PA

SI % Reduction In student Purchases

SI Percentage of children attending School

SVI

% of population that Registers for vouchers

Average Number of Hours of organised sports or activities Weekly

Average proportion attributed to vouchers

Average MET for sports Inputs

Relapse Rate

SVI Scale up factor

PE Cordial

PE Fruit Drink

PE Soft drink

Relapse Rate

SSB Scale up factor

SSB Tax %

# lsa_int_ssb_df %>%
#   filter(year==2057) %>%
#   filter(grepl("SSB Tax", focus)) %>%
#   filter(as.numeric(age.groups.output)<4) %>%
#   select(input.value, int.value)
# ggplot()+
#   geom_point(aes(x=input.value, y=int.value, color=gender.output), size=1)+
#   geom_line(aes(x=input.value, y=int.value, color=gender.output))+
#   geom_hline(aes(yintercept=det.int.value), linetype="dashed")+
# facet_grid( rows = vars(bmi.output), cols = vars(age.groups.output,), scale="free")+
#   theme_bw()+theme(legend.position = "bottom")+
#   guides(colour = guide_legend("BMI"))+
#   ggtitle("Discretionary foods")+ylab("Prevalence")+
#   xlab("Input assumption")+
#   scale_y_continuous(labels = scales::percent)